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Record W2009216269 · doi:10.1016/j.sbspro.2013.10.413

Teaching of Simulation an Adjustable Speed Drive of Induction Motor Using MATLAB/Simulink in Advanced Electrical Machine Laboratory

2013· article· en· W2009216269 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia - Social and Behavioral Sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsLakehead University
Fundersnot available
KeywordsMATLABInduction motorComputer scienceStepper motorControl engineeringSimulationEngineeringElectrical engineeringMechanical engineeringVoltageProgramming language

Abstract

fetched live from OpenAlex

The simulation of motor complicated applications conventionally can be a challenge for both undergraduate and postgraduate levels. To easy implementation for several kinds of control structures of an induction motor (IM) drive, some simulators such as MATLAB/Simulink to be necessary-especially for students-to develop and test various motor control algorithms in conducting electrical machines courses. In this paper, how to teach and simulate an adjustable speed drive of IM using Simulink blocks for an indirect field-oriented control (IFOC) algorithm is presented. The effectiveness of the adjustable IM drive is verified by simulation results at different operating conditions over a wide speed range.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.325
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it